Abstract: We consider the wireless senor network which is equipped with an energy harvesting device and a rechargeable battery, and the aim is to enable the sensor having the ability of adaptively adjusting the data sampling rate according to the available energy. Since the stochastic nature of the harvested energy, we define the energy deficiency probability for equivalently characterizing an information acquisition metric. We formulate the problem of adjusting data sampling rate as a constrained optimization problem, maximizing the data sampling rate while keeping the energy deficiency probability below a threshold. The classic large deviation theory is invoked for estimating the energy deficiency probability. Our experimental results verify that the algorithms proposed have the adaptation capability to accommodate both the energy-dynamics and the channel-dynamics for improving the information acquisition.

Key words: wireless sensor network, energy harvesting, large deviation theory, online measurement